Computational Intelligence for Genomics Data

1st Edition - January 21, 2025
Editors: Babita Pandey, Valentina Emilia Balas, Suman Lata Tripathi, Devendra Kumar Pandey, Mufti Mahmud
Language: English
Paperback ISBN:
9 7 8 - 0 - 4 4 3 - 3 0 0 8 0 - 6
eBook ISBN:
9 7 8 - 0 - 4 4 3 - 3 0 0 8 1 - 3

Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the develo… Read more

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Computational Intelligence for Genomics Data presents an overview of machine learning and deep learning techniques being developed for the analysis of genomic data and the development of disease prediction models. The book focuses on machine and deep learning techniques applied to dimensionality reduction, feature extraction, and expressive gene selection. It includes designs, algorithms, and simulations on MATLAB and Python for larger prediction models and explores the possibilities of software and hardware-based applications and devices for genomic disease prediction. With the inclusion of important case studies and examples, this book will be a helpful resource for researchers, graduate students, and professional engineers.

Title of Book
Cover image
Title page
Table of Contents
Copyright
List of contributors
About the editors
Preface
Key features
Acknowledgment
Section 1: Introduction to biological data and analysis
Chapter 1-1. Genomic data
Abstract
1.1.1 Introduction
1.1.2 Types of genomic data
References
Chapter 1-2. Extensive sequence analysis: revealing genomic knowledge throughout various domains
Abstract
1.2.1 Introduction
1.2.2 Research Methodologies
1.2.3 Results and discussion
1.2.4 Conclusion
References
Chapter 1-3. Integrative omics analysis using graph theoretical framework
Abstract
1.3.1 Introduction
1.3.2 Literature review
1.3.3 Graph theoretical foundations
1.3.4 Graph Theoretical Framework
1.3.5 Applications and impact
1.3.6 Conclusion and future directions
References
Chapter 1-4. Gene prioritization for cancer module identification
Abstract
1.4.1 Introduction
1.4.2 Method
1.4.3 Experimental results and analysis
1.4.4 Conclusion
References
Section 2: Traditional machine learning models for gene selection and classification
Chapter 2-1. Transforming disease classification: leveraging machine learning for Gene selection
Abstract
2.1.1 The gene selection conundrum
2.1.2 Enter the machine learning knights
2.1.3 Conclusion
References
Chapter 2-2. Study on breast cancer detection and classification using artificial intelligence techniques and sensors
Abstract
2.2.1 Introduction
2.2.2 Screening techniques of breast cancer
2.2.3 Genomics of breast cancer
2.2.4 Classification of breast cancer by artificial intelligence using image and genomics dataset
2.2.5 Evolution of biosensors and wearable sensors to detect breast cancer
2.2.6 Conclusion and future work
References
Chapter 2-3. Deep learning techniques for gene selection and cancer classification: a detailed review
Abstract
2.3.1 Introduction
2.3.2 Introduction to deep learning
2.3.3 Fundamentals of gene selection and cancer classification
2.3.4 Deep learning techniques for gene selection
2.3.5 Deep learning models for cancer classification
2.3.6 Performance evaluation and benchmarking
2.3.7 Interpretability and explainability of deep learning models
2.3.8 Case studies and applications
2.3.9 Conclusion
References
Section 3: Deep learning models for gene selection and classification
Chapter 3-1. Chronic kidney disease: causes, treatment, management, and future scope
Abstract
3.1.1 Introduction
3.1.2 Anatomy of the kidney
3.1.3 Genomic applications in healthcare
3.1.4 Causes of kidney disease
3.1.5 Types of kidney disease
3.1.6 Common kidney disease complications and stages
3.1.7 Risk factors and preventives measures of kidney disease
3.1.8 Conclusion and future scope
References
Chapter 3-2. Liver diseases: genetic factors, current challenges, and future directions
Abstract
3.2.1 Introduction
3.2.2 Gender disparity in liver disease mortality
3.2.3 Perspectives on acute and chronic diseases affecting liver, genetic and cell morphology
3.2.4 Genetic factors affecting liver
3.2.5 Alcohol-related liver disease
3.2.6 Anatomy and function of the liver
3.2.7 Symptoms of liver disease and risk factors
3.2.8 Stages of liver diseases severity
3.2.9 Risk factors of liver disease
3.2.10 Conclusion and future scope
References
Chapter 3-3. Principal component analysis and optimization -based feature selection and extraction for gene expression microarray: a comparative study of classification of neuromuscular disorder
Abstract
3.3.1 Introduction
3.3.2 Related work
3.3.3 Proposed methodology
3.3.4 Experiment and result
3.3.5 Performance evaluation parameters
3.3.6 Discussion
3.3.7 Conclusion
References
Section 4: Gene selection and classification using artificial intelligence-based optimization methods
Chapter 4-1. Gene selection and liver classification using machine learning
Abstract
4.1.1 Introduction
4.1.2 Background study
4.1.3 Importance of gene selection in machine learning
4.1.4 Gene selection using machine learning algorithms
4.1.5 Liver disease classification models
4.1.6 Description of the selected genes associated with liver diseases
4.1.7 Performance metrics for the developed classification models
4.1.8 Interpretation of gene expression patterns and their implications for liver disease diagnosis
4.1.9 Conclusion
References
Chapter 4-2. Machine learning algorithms for classification of cancer
Abstract
4.2.1 Introduction
4.2.2 Data types in cancer classification
4.2.3 Supervised learning algorithms in cancer classification
4.2.4 Deep learning techniques for cancer classification
4.2.5 Unsupervised learning algorithms and cancer clustering
4.2.6 Challenges and future perspectives in machine learning algorithms for classification of cancer
4.2.7 Conclusion
References
Chapter 4-3. Fusion of autoencoder model for gene predication and RNA disease association
Abstract
4.3.1 Introduction
4.3.2 Methodology based on fusion neural networks for association prediction
4.3.3 Experimental comparison and result analysis
4.3.4 Case study
4.3.5 Conclusion
References
Section 5: Explainable AI for computational biology
Chapter 5-1. Role of computational biology in the diagnosis of neurodegenerative disorders
Abstract
5.1.1 Introduction
5.1.2 Basics of computational biology
5.1.3 Applications of computational biology in neurodegenerative disorder diagnosis
5.1.4 Challenges and opportunities
5.1.5 Conclusion
References
Chapter 5-2. Artificial intelligence's applicability in cardiac imaging
Abstract
5.2.1 Introduction
5.2.2 Research and methodologies
5.2.3 Results and discussions
5.2.4 Conclusion
References
Section 6: Applications of computational biology in health
Chapter 6-1. Diagnosis of liver disorder
Abstract
6.1.1 Introduction
6.1.2 Focus on the identification of liver disorders
6.1.3 Emergence of computational biology as a revolutionary methodology
6.1.4 Liver disorders: a global health burden
6.1.5 Computational biology: tools and techniques
6.1.6 Application of computation models in liver disorders
6.1.7 Enhancing precision in diagnosis and treatment
6.1.8 Transformation in healthcare through computational biology
6.1.9 Conclusion
References
Chapter 6-2. Diagnosis of neuromuscular disorder
Abstract
6.2.1 Introduction
6.2.2 Computational biology’s role in diagnostic transformation
6.2.3 Analysis of complex genetic data
6.2.4 Incorporation of multiomics data
6.2.5 Predictive modeling for disease progression
6.2.6 Computational tools for image analysis
6.2.7 Detection and characterization of structural irregularities
6.2.8 Support for clinicians in making precise and prompt diagnoses
6.2.9 Significance and future implications
6.2.10 Conclusion
References
Chapter 6-3. Diagnosis of autism spectrum disorder—prediction and analysis of enhanced classification model based on machine learning
Abstract
6.1 Introduction
6.2 Background and literature review
6.3 Etiology and prevalence of ASD
6.4 Basic concepts of machine learning
6.5 Data attainment and description
6.6 Data preprocessing
6.7 Model development and training
6.8 Evaluation metrics
6.9 Results and discussion
6.10 Conclusion and discussion
References
Chapter 6-4. Healthcare applications of computational genomics
Abstract
6.4.1 Introduction
6.4.2 Research methodologies
6.4.3 Results and discussions
6.4.4 Conclusions
References
Chapter 6-5. Diagnosis of liver disorder
Abstract
6.5.1 Introduction
6.5.2 Medical history and physical examination
6.5.3 Laboratory tests
6.5.4 Imaging studies
6.5.5 Advances in diagnostic techniques
6.5.6 Serological assays
6.5.7 Integration of artificial intelligence and machine learning
6.5.8 Personalized medicine and genetic testing
6.5.9 Case studies
6.5.10 Challenges and future directions
6.5.11 Limitations of current diagnostic approaches
6.5.12 Future perspectives and emerging technologies
6.5.13 Conclusion
References
Index

Babita Pandey

Babita Pandey completed her PhD in the field of artificial intelligence in biomedical signal processing from IIT-BHU, Varanasi. Currently, she is an Associate Professor of Computer Science at Babasaheb Bhimrao Ambedkar University, Lucknow. She has published more than 150 research papers in refereed journals and conferences. She has delivered many expert lectures for students as well as in faculty development programs. She has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in various international/national conferences. She received the “Research Excellence Award” in 2014, 2015, 2016 from Lovely Professional University and the “Research and Academic Excellence Award” in 2021, 2022 at Babasaheb Bhimrao Ambedkar University, Lucknow, India. She also received the P.D. Sethi Award 2022. She edited one book on link prediction in social networks. Her areas of research include link prediction, dimensionality reduction of genomic data, e-learning, machine learning and deep learning deployed for images.

Affiliations and expertise

Babasaheb Bhimrao Ambedkar University, Lucknow, India

Valentina Emilia Balas

Valentina Emilia Balas is currently a Full Professor in the Department of Automatics and Applied Software at the Faculty of Engineering, “Aurel Vlaicu” University of Arad, Romania. She holds a PhD cum Laude in Applied Electronics and Telecommunications from the Polytechnic University of Timisoara. Dr. Balas is the author of more than 350 research papers. She is the Editor-in-Chief of the 'International Journal of Advanced Intelligence Paradigms' and the 'International Journal of Computational Systems Engineering', an editorial board member for several other national and international publications, and an expert evaluator for national and international projects and PhD theses.

Affiliations and expertise

Full Professor, Department of Automatics and Applied Software, Faculty of Engineering, "Aurel Vlaicu" University of Arad, Arad, Romania

Suman Lata Tripathi

Suman Lata Tripathi completed her PhD in the area of microelectronics and VLSI from MNNIT, Allahabad. She was also a remote post-doc researcher at Nottingham Trent University, London, UK in 2022. She is a Professor at Lovely Professional University with more than 19 years of experience in academics. She has published more than 89 research papers in refereed journals and conferences. She has also published 13 Indian patents and 2 copyrights. She has organized several workshops, summer internships, and expert lectures for students. She has worked as a session chair, conference steering committee member, editorial board member, and peer reviewer in international/national conferences. She received the “Research Excellence Award” in 2019 and “Research Appreciation Award” in 2020, 2021 at Lovely Professional University, India. She also received funded projects from SERB DST under the scheme TARE in the area of Microelectronics devices. She has edited or authored more than 15 books in different areas of Electronics and electrical engineering. Her areas of expertise includes microelectronics device modeling and characterization, low power VLSI circuit design, VLSI design of testing, and advanced FET design for IoT, Embedded System Design, reconfigurable architecture with FPGAs and biomedical applications.

Affiliations and expertise

Lovely Professional University, Phagwara, Punjab, India

Devendra Kumar Pandey

Devendra Kumar Pandey completed his PhD in Bioprocess Engineering from IIT (BHU), Varanasi. He has been a Professor at Lovely Professional University for more than 22 years. He has published more than 112 research papers in refereed journals and conferences. He received the “Research Excellence Award” in 2019 and “Research Appreciation Award” in 2020, 2021 at Lovely Professional University, India. He has also received funded projects from UPCST under the area of Medicinal Plants. His areas of expertise include Bioprocess Engineering, Medicinal Plant Biotechnology, Plant-Microbe interaction, Computational Biology.

Affiliations and expertise

Lovely Professional University, Phagwara, Punjab, India

Mufti Mahmud

Mufti Mahmud is an Associate Professor of Cognitive Computing at the Department of Computer Science of Nottingham Trent University (NTU). Dr. Mahmud was appointed to the USET, University Shadow Executive Team, in 2022, providing specialist input to the University Executive Team and Vice-Chancellor on strategic policy and direction matters related to Equality, Diversity & Inclusion (EDI). He is the Coordinator of the Computer Science and Informatics Unit of Assessment of Research Excellence Framework at NTU and the deputy group leader of the Interactive Systems Research Group (ISRG) and the Cognitive Computing & Brain Informatics (CCBI) research group. He is also an active member of the Computing and Informatics Research Centre (CIRC) and the Medical Technologies Innovation Facility (MTIF). He is a member of the NTU Distance Learning Governance, Operation and Steering committee as well as the International Mobility Committee and serves as an independent end-point assessor for the Level 6 BSc (Hons) in Digital & Technology Solutions Professional Degree Apprenticeship, and an expert of the online master's degree in computer science. He led the teaching of the Big Data and its Infrastructures (Postgraduate – on-campus and online delivery) module. He is a Fellow of the Higher Education Academy, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE) and the Association of Computing Machinery (ACM), and a professional member of the British Computer Society (BCS).

Affiliations and expertise

Department of Computer Science and Engineering, Nottingham Trent University, UK

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Computational Intelligence for Genomics Data

Purchase options

Institutional subscription on ScienceDirect

Babita Pandey

Valentina Emilia Balas

Suman Lata Tripathi

Devendra Kumar Pandey

Mufti Mahmud